UXarray for Advanced HEALPix Analysis & Visualization

In this section, you’ll learn:

Using the uxarray package to perform advanced analysis operators over HEALPix data such as non-conservative zonal means, etc.

Prerequisites

Concepts	Importance	Notes
UXarray	Necessary
HEALPix overview	Necessary

Time to learn: 30 minutes

import cartopy.crs as ccrs
import holoviews as hv
import intake
import uxarray as ux

Rasterized Point Plots

When working with a higher-resolution dataset at a global scale, it’s not always practical to render each cell as a polygon. Instead, we can rasterize the center of each pixel.

projection = ccrs.Robinson()

# Controls the size of each pixel (smaller value leads to larger pixels)
pixel_ratio = 0.5

uxda.isel(time=0).plot.points(
    projection=projection,
    rasterize=True,
    dynamic=False,
    width=1000,
    height=500,
    pixel_ratio=pixel_ratio,
    cmap="inferno",
    title=f"Global Air Temperature, pixel_ratio={pixel_ratio}",
)

If we decrease the size of each pixel (by setting the pixel ratio to a higher value), we can start to see missing values, which is due to a lower density of points near the poles, leading to some pixels not containing any of our original points.

Because of this, it’s usefully to try a few pixel_ratio values and see which one works best for your given resolution.

projection = ccrs.Robinson()

# Controls the size of each pixel (smaller value leads to larger pixels)
pixel_ratio = 2.0

uxda.isel(time=0).plot.points(
    projection=projection,
    rasterize=True,
    dynamic=False,
    width=1000,
    height=500,
    pixel_ratio=pixel_ratio,
    cmap="inferno",
    title=f"Global Air Temperature, pixel_ratio={pixel_ratio}",
)

Non-conservative Zonal mean

Calculating the zonal mean is easy by providing the latitude range between -90 and 90 degrees with a step size in degrees:

zonal_mean_tas = uxda.isel(time=0).zonal_mean(lat=(-90, 90, 5))

(
    uxda.isel(time=0).plot(
        cmap="inferno",
        # periodic_elements="split",
        height=300,
        width=600,
        colorbar=False,
        ylim=(-90, 90),
    )
    + zonal_mean_tas.plot.line(
        x="tas_zonal_mean",
        y="latitudes",
        height=300,
        width=180,
        ylabel="",
        ylim=(-90, 90),
        xlim=(220, 310),
        # xticks=[220, 250, 280, 310],
        yticks=[-90, -45, 0, 45, 90],
        grid=True,
    )
).opts(title="Temperature and its Zonal means at every 5 degrees latitude")

Remapping

Now, we will be looking into a remapping case. The data set we are using in this section has the zoom=10 available but not for all the variables, e.g. tas has all NaN values at that zoom level. Let us try to remap our data here that is sampled at zoom=9 to a Grid at zoom=10.

Let’s start with creating the destination uxarray.Grid:

%%time
uxgrid_zoom10 = ux.Grid.from_healpix(zoom=10, pixels_only=False)

CPU times: user 19.6 s, sys: 5.01 s, total: 24.6 s
Wall time: 10.4 s

%%time
uxda_remapped = uxda.isel(time=0).remap.inverse_distance_weighted(
    uxgrid_zoom10, k=3, remap_to="face centers"
)

CPU times: user 8min 50s, sys: 700 ms, total: 8min 51s
Wall time: 8min 51s

%%time

uxda_remapped.plot(
    projection=projection,
    cmap="inferno",
    features=["borders", "coastline"],
    title="Global temperature - remapped to zoom=10",
    width=700,
)

CPU times: user 34.2 s, sys: 1.78 s, total: 36 s
Wall time: 36.3 s

WARNING:param.GeoOverlayPlot01861: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.

UXarray for Advanced HEALPix Analysis & Visualization

In this section, you’ll learn:

Prerequisites

Open data catalog

Create UXarray Datasets from HEALPix

Data variable of interest

Global mean and plot

Rasterized Point Plots

Cross-sections

Latitude interval

Non-conservative Zonal mean

Remapping

UXarray for Advanced HEALPix Analysis & Visualization

In this section, you’ll learn:

Related Documentation

Prerequisites

Open data catalog

Create UXarray Datasets from HEALPix

Data variable of interest

Global mean and plot

Rasterized Point Plots

Cross-sections

Latitude interval

Non-conservative Zonal mean

Remapping